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getting started -3 questions

Posted on 2007-11-25
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Last Modified: 2008-02-01
Hi. I have the following three questions:

Question 1)  
I have a file called recommendations.py in my the C:\Python25\Lib folder.
I also edited my environment path variable to include C:\Python25;
I went to the Python Command line utility and typed in
run recommendations at the triple prompt and I get the error message
File "<stdin>" line 1
run recommendations
                                 ^
SyntaxError: invalid syntax.

Can you tell me what I am doing wrong?

Question 2)
  Also, can someone explain what the following means?
"working with Python interactively in this chapter, so you should save
recommendations.py somewhere where the Python interactive interpreter can find it.
This could be in the python/Lib directory, but the easiest way to do it is to start the
Python interpreter in the same directory in which you saved the file."


How do i start the Python interpreter in the same directory ?



Question 3)  
Finally does anyone have any idea what is meant by the ... dots  in the statement below?

reload(recommendations)
>>> recommendations.sim_distance(recommendations.critics,
... 'Lisa Rose','Gene Seymour')

 I tried recommendations.sim_distance(recommendations.critics,'Lisa Rose','Gene Seymour')

but I get an error:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'recommendations' is not defined.
recommendations
Thank you.



The file contents are below:


# A dictionary of movie critics and their ratings of a small
# set of movies
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
 'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
 'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
 'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
 'You, Me and Dupree': 3.5},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
 'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
 'The Night Listener': 4.5, 'Superman Returns': 4.0,
 'You, Me and Dupree': 2.5},
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
 'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
 'You, Me and Dupree': 2.0},
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
 'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}


from math import sqrt

# Returns a distance-based similarity score for person1 and person2
def sim_distance(prefs,person1,person2):
  # Get the list of shared_items
  si={}
  for item in prefs[person1]:
    if item in prefs[person2]: si[item]=1

  # if they have no ratings in common, return 0
  if len(si)==0: return 0

  # Add up the squares of all the differences
  sum_of_squares=sum([pow(prefs[person1][item]-prefs[person2][item],2)
                      for item in prefs[person1] if item in prefs[person2]])

  return 1/(1+sum_of_squares)

# Returns the Pearson correlation coefficient for p1 and p2
def sim_pearson(prefs,p1,p2):
  # Get the list of mutually rated items
  si={}
  for item in prefs[p1]:
    if item in prefs[p2]: si[item]=1

  # if they are no ratings in common, return 0
  if len(si)==0: return 0

  # Sum calculations
  n=len(si)
 
  # Sums of all the preferences
  sum1=sum([prefs[p1][it] for it in si])
  sum2=sum([prefs[p2][it] for it in si])
 
  # Sums of the squares
  sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
  sum2Sq=sum([pow(prefs[p2][it],2) for it in si])      
 
  # Sum of the products
  pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
 
  # Calculate r (Pearson score)
  num=pSum-(sum1*sum2/n)
  den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
  if den==0: return 0

  r=num/den

  return r

# Returns the best matches for person from the prefs dictionary.
# Number of results and similarity function are optional params.
def topMatches(prefs,person,n=5,similarity=sim_pearson):
  scores=[(similarity(prefs,person,other),other)
                  for other in prefs if other!=person]
  scores.sort()
  scores.reverse()
  return scores[0:n]

# Gets recommendations for a person by using a weighted average
# of every other user's rankings
def getRecommendations(prefs,person,similarity=sim_pearson):
  totals={}
  simSums={}
  for other in prefs:
    # don't compare me to myself
    if other==person: continue
    sim=similarity(prefs,person,other)

    # ignore scores of zero or lower
    if sim<=0: continue
    for item in prefs[other]:
          
      # only score movies I haven't seen yet
      if item not in prefs[person] or prefs[person][item]==0:
        # Similarity * Score
        totals.setdefault(item,0)
        totals[item]+=prefs[other][item]*sim
        # Sum of similarities
        simSums.setdefault(item,0)
        simSums[item]+=sim

  # Create the normalized list
  rankings=[(total/simSums[item],item) for item,total in totals.items()]

  # Return the sorted list
  rankings.sort()
  rankings.reverse()
  return rankings

def transformPrefs(prefs):
  result={}
  for person in prefs:
    for item in prefs[person]:
      result.setdefault(item,{})
     
      # Flip item and person
      result[item][person]=prefs[person][item]
  return result


def calculateSimilarItems(prefs,n=10):
  # Create a dictionary of items showing which other items they
  # are most similar to.
  result={}
  # Invert the preference matrix to be item-centric
  itemPrefs=transformPrefs(prefs)
  c=0
  for item in itemPrefs:
    # Status updates for large datasets
    c+=1
    if c%100==0: print "%d / %d" % (c,len(itemPrefs))
    # Find the most similar items to this one
    scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance)
    result[item]=scores
  return result

def getRecommendedItems(prefs,itemMatch,user):
  userRatings=prefs[user]
  scores={}
  totalSim={}
  # Loop over items rated by this user
  for (item,rating) in userRatings.items( ):

    # Loop over items similar to this one
    for (similarity,item2) in itemMatch[item]:

      # Ignore if this user has already rated this item
      if item2 in userRatings: continue
      # Weighted sum of rating times similarity
      scores.setdefault(item2,0)
      scores[item2]+=similarity*rating
      # Sum of all the similarities
      totalSim.setdefault(item2,0)
      totalSim[item2]+=similarity

  # Divide each total score by total weighting to get an average
  rankings=[(score/totalSim[item],item) for item,score in scores.items( )]

  # Return the rankings from highest to lowest
  rankings.sort( )
  rankings.reverse( )
  return rankings

def loadMovieLens(path='/data/movielens'):
  # Get movie titles
  movies={}
  for line in open(path+'/u.item'):
    (id,title)=line.split('|')[0:2]
    movies[id]=title
 
  # Load data
  prefs={}
  for line in open(path+'/u.data'):
    (user,movieid,rating,ts)=line.split('\t')
    prefs.setdefault(user,{})
    prefs[user][movies[movieid]]=float(rating)
  return prefs
0
Comment
Question by:onyourmark
  • 3
4 Comments
 

Author Comment

by:onyourmark
ID: 20345426
Hi, I forgot to say what the function below was:
from math import sqrt
# Returns a distance-based similarity score for person1 and person2
def sim_distance(prefs,person1,person2):
# Get the list of shared_items
si={}
for item in prefs[person1]:
if item in prefs[person2]:
si[item]=1
# if they have no ratings in common, return 0
if len(si)==0: return 0
# Add up the squares of all the differences
sum_of_squares=sum([pow(prefs[person1][item]-prefs[person2][item],2)
for item in prefs[person1] if item in prefs[person2]])
return 1/(1+sum_of_squares)
0
 
LVL 28

Expert Comment

by:pepr
ID: 20348517
For the question 1: The Python interpreter does not know any command like "run". You should launch the script from cmd console window by typing explicitly (say we are in C:\tmp directory, you can see the prompt...):

C:\tmp>python recommendations.py

The .py extension is usually (by the python installer) associated with the Python, so you can also type it without "python"

C:\tmp>recommendations.py

Or you can double click the recommendations.py icon in the Explorer window.

If you insist on launching the module from the Python interactive mode, you can use runpy module (new in Python 2.5) this way:

>>> import runpy
>>> runpy.run_module('recommendations.py')

(see http://docs.python.org/lib/module-runpy.html), but this is not the usual way. There are also other ways, but you probably do not want to try them.
0
 
LVL 28

Expert Comment

by:pepr
ID: 20348536
The first part of my above answer should answer also your question 2. In the cmd window (you get it in Windows by launching the "Command prompt" icon) the same way how you launch the interactive mode from the cmd window. You just type the Python program name as the argument of the Python interpreter.

For question 3, the '...' is a special prompt in the interactive mode. It says that the interpreter is waiting for continuation of the command. In your case, just press Enter once more. The entered empty line will finish the command and it will be processed by the interpreter.

See also "2. Using the Python Interpreter" in standard tutorial (http://docs.python.org/tut/node4.html).
0
 
LVL 28

Accepted Solution

by:
pepr earned 250 total points
ID: 20348539
Also, try simpler examples first to get used to. Follow the above mentioned tutorial http://docs.python.org/tut/tut.html
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